# Computations
import numpy as np
import pandas as pd
from scipy.stats import norm

# preprocessing
from sklearn.impute import SimpleImputer
import re

## progress bar
import progressbar

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## WordCloud
from wordcloud import WordCloud


import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

Telco Customer Churn

• Preprocessing
• Exploratory Data Analysis
• Modeling: Decision Tree Classifier with Voting Classifier
• Modeling: PyTorch Multi-layer Perceptron (MLP)

Telco Customer Churn

In this article, we analyze and predict customer churn for Telco Customer Churn data.

Dataset

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device * protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Columns	Description
customerID	Customer ID
gender	Whether the customer is a male or a female
SeniorCitizen	Whether the customer is a senior citizen or not (1, 0)
Partner	Whether the customer has a partner or not (Yes, No)
Dependents	Whether the customer has dependents or not (Yes, No)
tenure	Number of months the customer has stayed with the company
PhoneService	Whether the customer has a phone service or not (Yes, No)
MultipleLines	Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService	Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity	Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup	Whether the customer has an online backup or not (Yes, No, No internet service)
DeviceProtection	Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport	Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV	Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies	Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract	The contract term of the customer (Month-to-month, One year, Two years)
PaperlessBilling	Whether the customer has paperless billing or not (Yes, No)
PaymentMethod	The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges	The amount charged to the customer monthly
TotalCharges	The total amount charged to the customer
Churn	Whether the customer churned or not (Yes or No)

Data = pd.read_csv('telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')

def Data_info(Inp, Only_NaN = False):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out ['Size'] = Inp.shape[0]
    Out['Percentage'] = 100 - np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    Out.index.name = 'Features'
    Out['Data Type'] = Out['Data Type'].astype(str)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out

def text_sep(txt): return re.sub(r"(\w)([A-Z])", r"\1 \2", txt)

def dtypes_group(Inp):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Columns'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Columns'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
    return Out

def col_details(Col):
    print(Back.BLACK + Fore.CYAN + Style.NORMAL + '%s:' % text_sep(Col))
    print(Style.RESET_ALL)
    print('%s' % ', '.join(Data[Col].unique()))
    
data_info = Data_info(Data).reset_index(drop = False)
fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type', text = 'Data Type',
            color_discrete_sequence = ['PaleGreen', 'LightBlue', 'PeachPuff'], hover_data = data_info.columns)
fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1, y=.5, traceorder="normal",
                                                     bordercolor="DarkGray", borderwidth=1), width = 980)
fig.update_traces(texttemplate= 6*' ' + '%{label}', textposition='inside')
fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
fig.show()
   
Data.rename(columns = {'gender':'Gender', 'tenure':'Tenure'}, inplace = True)
Data.columns = [text_sep(txt) for txt in Data.columns.tolist()]

Preprocessing

Int Columns

Data_types = dtypes_group(Data)
display(Data_types)
Temp = Data_types.loc[Data_types.index == 'int64'].values[0,0]
Data[Temp] = Data[Temp].astype(int)
del Temp

	Columns
int64	[Senior Citizen, Tenure]
float64	[Monthly Charges]
object	[customer ID, Payment Method, Paperless Billin...

Float Columns

Temp = Data_types.loc[Data_types.index == 'float64'].values[0,0]
Data[Temp] = Data[Temp].astype(float)
del Temp
Data['Total Charges'] = pd.to_numeric(Data['Total Charges'], errors='coerce')

Yes/No Columns

First, let's convert all Yes/No columns using as follows

\begin{cases} 0 &\mbox{No}\\ 1 &\mbox{Yes}\end{cases}

Temp = []
for i in Data_types.loc[Data_types.index == 'object'].values[0,0]:
    if set(Data[i].unique().tolist()) == {'No', 'Yes'}:
        Temp.append(i)
        
Data[Temp] = Data[Temp].replace({'Yes':1, 'No':0}).astype(int)
del Temp

However, some other columns can be converted similarly; however, we need to create a new feature.

Temp = []
for i in Data_types.loc[Data_types.index == 'object'].values[0,0]:
    if set(Data[i].unique().tolist()) == {'No', 'No internet service', 'Yes'}:
        Temp.append(i)
print(Back.BLACK + Fore.CYAN + Style.NORMAL + 'Columns:' +
          Style.RESET_ALL + ' %s' % ', '.join(Temp))

Columns: Streaming Movies, Streaming TV, Tech Support, Device Protection, Online Backup, Online Security

Note that,

col_details('Internet Service')

Internet Service:

DSL, Fiber optic, No

This Column can be coded as follows

$$\mbox{InternetServiceType} = \begin{cases} 0 &\mbox{No} \\ 1 &\mbox{DSL}\\ 2 &\mbox{Fiber optic}\end{cases}$$

def myfun(x):
    if x == 'No':
        return 0
    elif x == 'DSL':
        return 1
    else:
        return 2

Data['Internet Service'] = Data['Internet Service'].apply(lambda x: myfun(x)).astype(int)
del myfun

Since we have already included No interent service in InternetService, we can code the rest as,

\begin{cases} 0 &\mbox{No, No internet service}\\ 1 &\mbox{Yes}\end{cases}

Data[Temp] = Data[Temp].applymap(lambda x: 1 if x =='Yes' else 0).astype(int)

Since, there is already a feature as PhoneService, for MultipleLines, we can try $$ \mbox{MultipleLines} = \begin{cases} 0 &\mbox{No, No phone service}\\ 1 &\mbox{Yes}\end{cases} $$

Data['Multiple Lines'] = Data['Multiple Lines'].map(lambda x: 1 if x =='Yes' else 0).astype(int)

Other Columns

Data_types = dtypes_group(Data)
Temp = Data_types.loc[Data_types.index == 'object'].values[0,0]
print('Columns: %s' %', '.join(Temp))

Columns: Contract, Gender, Payment Method, customer ID

Contract

col_details('Contract')

Contract:

Month-to-month, One year, Two year

\begin{cases} 0 &\mbox{Month-to-month}\\ 1 &\mbox{One year}\\ 2 &\mbox{Two year} \end{cases}

Data['Contract'] = Data['Contract'].replace({'Month-to-month':0, 'One year':1, 'Two year':2}).astype(int)

Gender

$$ \mbox{Gender} = \begin{cases} 0 &\mbox{Female}\\ 1 &\mbox{Male}\end{cases} $$

Data['Gender'] = Data['Gender'].map(lambda x: 1 if x =='Male' else 0).astype(int)

PaymentMethod

col_details('Payment Method')

Payment Method:

Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic)

In this case, we can not rank these values. Therefore,

Data = Data.join(pd.get_dummies(Data['Payment Method']).astype(int))
Data = Data.drop(columns = ['Payment Method'])
Data_types = dtypes_group(Data)
display(Data_types)

	Columns
int32	[Device Protection, Credit card (automatic), B...
float64	[Monthly Charges, Total Charges]
object	[customer ID]

Imputing Missing Values

Temp = Data_info(Data, Only_NaN = True)
Temp = Temp.index.tolist()
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
Data[Temp] = imp.fit_transform(Data[Temp])

Data Correlations

Let's take a look at the variance of the features.

display(Data.drop(columns = ['Churn']).var().sort_values(ascending = False).to_frame(name= 'Variance')\
       .style.background_gradient(cmap=sns.light_palette("green", as_cmap=True)).set_precision(2))

	Variance
Total Charges	5130226.17
Monthly Charges	905.41
Tenure	603.17
Contract	0.70
Internet Service	0.61
Gender	0.25
Partner	0.25
Multiple Lines	0.24
Paperless Billing	0.24
Streaming Movies	0.24
Streaming TV	0.24
Online Backup	0.23
Device Protection	0.23
Electronic check	0.22
Dependents	0.21
Tech Support	0.21
Online Security	0.20
Mailed check	0.18
Bank transfer (automatic)	0.17
Credit card (automatic)	0.17
Senior Citizen	0.14
Phone Service	0.09

def Correlation_Plot (Df,Fig_Size):
    Correlation_Matrix = Df.corr().round(2)
    mask = np.zeros_like(Correlation_Matrix)
    mask[np.triu_indices_from(mask)] = True
    for i in range(len(mask)):
        mask[i,i]=0
    Fig, ax = plt.subplots(figsize=(Fig_Size,Fig_Size))
    sns.heatmap(Correlation_Matrix, ax=ax, mask=mask, annot=True, square=True, 
                cmap =sns.color_palette("Greens", n_colors=10), linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": .6})

Correlation_Plot (Data, 16)

Correlations of features with customer Churn.

Temp = Data.corr().round(2)
Temp['Churn'].sort_values().to_frame(name= 'Correlation')[:-1].style.background_gradient(cmap='RdYlGn',
                                                                                 subset=['Correlation']).set_precision(2)

	Correlation
Contract	-0.40
Tenure	-0.35
Total Charges	-0.20
Online Security	-0.17
Tech Support	-0.16
Dependents	-0.16
Partner	-0.15
Credit card (automatic)	-0.13
Bank transfer (automatic)	-0.12
Mailed check	-0.09
Online Backup	-0.08
Device Protection	-0.07
Gender	-0.01
Phone Service	0.01
Multiple Lines	0.04
Streaming TV	0.06
Streaming Movies	0.06
Senior Citizen	0.15
Paperless Billing	0.19
Monthly Charges	0.19
Electronic check	0.30
Internet Service	0.32

fig, ax = plt.subplots(nrows=1, ncols=3, figsize = (16, 6))
Temp = ['Tenure','Monthly Charges','Total Charges']

for i in range(len(Temp)):
    _ = sns.distplot(Data[Temp[i]], 
                     fit=norm, kde=False, color='seagreen', ax= ax[i])

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Saving

Data.to_csv('telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn_clean.csv', index=False)

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